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Spatiotemporal modeling of microbial metabolism. | LitMetric

Spatiotemporal modeling of microbial metabolism.

BMC Syst Biol

Department of Chemical Engineering, University of Massachusetts, 240 Thatcher Way, Life Science Laboratories Building, Amherst, MA, 01003, USA.

Published: March 2016

AI Article Synopsis

  • Spatiotemporal metabolic models are essential for understanding microbial systems in variable environments, yet they have seen limited development compared to steady-state models.
  • A new methodology integrates genome-scale metabolic reconstructions with transport equations to analyze these systems dynamically, which involves complex numerical techniques.
  • The approach is validated through practical examples, demonstrating its capacity for efficient and robust solutions in both industrial and clinical microbial contexts.

Article Abstract

Background: Microbial systems in which the extracellular environment varies both spatially and temporally are very common in nature and in engineering applications. While the use of genome-scale metabolic reconstructions for steady-state flux balance analysis (FBA) and extensions for dynamic FBA are common, the development of spatiotemporal metabolic models has received little attention.

Results: We present a general methodology for spatiotemporal metabolic modeling based on combining genome-scale reconstructions with fundamental transport equations that govern the relevant convective and/or diffusional processes in time and spatially varying environments. Our solution procedure involves spatial discretization of the partial differential equation model followed by numerical integration of the resulting system of ordinary differential equations with embedded linear programs using DFBAlab, a MATLAB code that performs reliable and efficient dynamic FBA simulations. We demonstrate our methodology by solving spatiotemporal metabolic models for two systems of considerable practical interest: (1) a bubble column reactor with the syngas fermenting bacterium Clostridium ljungdahlii; and (2) a chronic wound biofilm with the human pathogen Pseudomonas aeruginosa. Despite the complexity of the discretized models which consist of 900 ODEs/600 LPs and 250 ODEs/250 LPs, respectively, we show that the proposed computational framework allows efficient and robust model solution.

Conclusions: Our study establishes a new paradigm for formulating and solving genome-scale metabolic models with both time and spatial variations and has wide applicability to natural and engineered microbial systems.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4774267PMC
http://dx.doi.org/10.1186/s12918-016-0259-2DOI Listing

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